Semantic Similarity
405 papers with code • 8 benchmarks • 12 datasets
The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.
Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
Libraries
Use these libraries to find Semantic Similarity models and implementationsDatasets
Latest papers
Lightweight Embeddings for Graph Collaborative Filtering
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods.
Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks
To rigorously evaluate the quality of our automatic labeling framework, we contribute an algorithm SIMILARITY to produce two novel metrics, temporal similarity and semantic similarity.
RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain
Large Language Models (LLMs) increasingly support applications in a wide range of domains, some with potential high societal impact such as biomedicine, yet their reliability in realistic use cases is under-researched.
A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances
While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity.
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
To achieve this, we propose L2RM, a general framework based on Optimal Transport (OT) that learns to rematch mismatched pairs.
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising
Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images.
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
Semantic segmentation has innately relied on extensive pixel-level annotated data, leading to the emergence of unsupervised methodologies.
The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations
When deriving contextualized word representations from language models, a decision needs to be made on how to obtain one for out-of-vocabulary (OOV) words that are segmented into subwords.
Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric
Medical language processing and deep learning techniques have emerged as critical tools for improving healthcare, particularly in the analysis of medical imaging and medical text data.
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages
Exploring and quantifying semantic relatedness is central to representing language.